---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8033333333333333
    - name: Precision
      type: precision
      value: 0.7970708748615725
    - name: Recall
      type: recall
      value: 0.8033333333333333
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swin-tiny-patch4-window7-224

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4788
- Accuracy: 0.8033
- Precision: 0.7971
- Recall: 0.8033
- F1 Score: 0.7802

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5946          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 2.0   | 8    | 0.6006          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 3.0   | 12   | 0.5677          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 4.0   | 16   | 0.5616          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 5.0   | 20   | 0.5556          | 0.75     | 0.7193    | 0.75   | 0.7023   |
| No log        | 6.0   | 24   | 0.5435          | 0.7667   | 0.7819    | 0.7667 | 0.7019   |
| No log        | 7.0   | 28   | 0.5318          | 0.7792   | 0.7885    | 0.7792 | 0.7281   |
| 0.5745        | 8.0   | 32   | 0.5316          | 0.7542   | 0.7262    | 0.7542 | 0.7126   |
| 0.5745        | 9.0   | 36   | 0.5232          | 0.7667   | 0.7533    | 0.7667 | 0.7185   |
| 0.5745        | 10.0  | 40   | 0.5226          | 0.7708   | 0.7639    | 0.7708 | 0.7217   |
| 0.5745        | 11.0  | 44   | 0.5217          | 0.7708   | 0.7597    | 0.7708 | 0.7253   |
| 0.5745        | 12.0  | 48   | 0.5224          | 0.7625   | 0.7561    | 0.7625 | 0.7034   |
| 0.5745        | 13.0  | 52   | 0.5213          | 0.7708   | 0.7510    | 0.7708 | 0.7409   |
| 0.5745        | 14.0  | 56   | 0.5207          | 0.7667   | 0.7709    | 0.7667 | 0.7064   |
| 0.4741        | 15.0  | 60   | 0.5247          | 0.7583   | 0.7343    | 0.7583 | 0.7334   |
| 0.4741        | 16.0  | 64   | 0.5352          | 0.7708   | 0.7639    | 0.7708 | 0.7217   |
| 0.4741        | 17.0  | 68   | 0.5227          | 0.7708   | 0.7507    | 0.7708 | 0.7460   |
| 0.4741        | 18.0  | 72   | 0.5206          | 0.7583   | 0.7564    | 0.7583 | 0.6912   |
| 0.4741        | 19.0  | 76   | 0.5088          | 0.775    | 0.7627    | 0.775  | 0.7353   |
| 0.4741        | 20.0  | 80   | 0.5144          | 0.7667   | 0.7503    | 0.7667 | 0.7221   |
| 0.4741        | 21.0  | 84   | 0.5227          | 0.7875   | 0.7918    | 0.7875 | 0.7453   |
| 0.4741        | 22.0  | 88   | 0.5150          | 0.775    | 0.7564    | 0.775  | 0.7494   |
| 0.4233        | 23.0  | 92   | 0.5240          | 0.7667   | 0.7533    | 0.7667 | 0.7185   |
| 0.4233        | 24.0  | 96   | 0.5156          | 0.7792   | 0.7684    | 0.7792 | 0.7418   |
| 0.4233        | 25.0  | 100  | 0.5141          | 0.7792   | 0.7631    | 0.7792 | 0.7503   |
| 0.4233        | 26.0  | 104  | 0.5234          | 0.7833   | 0.7813    | 0.7833 | 0.7420   |
| 0.4233        | 27.0  | 108  | 0.5175          | 0.7833   | 0.7813    | 0.7833 | 0.7420   |
| 0.4233        | 28.0  | 112  | 0.5122          | 0.7958   | 0.7856    | 0.7958 | 0.7715   |
| 0.4233        | 29.0  | 116  | 0.5126          | 0.7958   | 0.7856    | 0.7958 | 0.7715   |
| 0.3931        | 30.0  | 120  | 0.5130          | 0.7958   | 0.7856    | 0.7958 | 0.7715   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3